Forcible Displacement Statistics
Intro
Seven Key Questions
addressed through predefined crafted static charts
What is the share of Forcibly Displaced People among total Migrants?
How the different Categories of Forcibly Displaced People are evolving over time?
What are the main Countries of Origin of Forcibly Displaced People?
What are the Demographics profiles of Forcibly Displaced People in relation with the host population?
What are the trends in term of Solutions in this Country?
Is the asylum Processing capacity in relation with the demand?
If happening, what is the evolution of a country as a displacement Source?
Charts can easily be copied as an image as is, or adjusted using a reproducible source code in R statistical language.
For instance, you can change the title from a key question to a key message or add annotations to support data storytelling.
The resulting charts can be embedded in reports, fact-sheets, social media or presentations to accompany a narrative / campaign. More information on Forcible Displacement Statistic is available with UNHCR here and through International Statistical Recommendations on refugee / asylum seekers and Internally Displaced People
World
lastyear <- 2021Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter( Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = " ",
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter( Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin ","" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter( Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in" ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People" ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = "",
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(Year == lastyear) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decisionGlobal.html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decisionGlobal.html"),
paste0(getwd(),"/decision/decisionGlobal.png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries source of Forcibly Displaced People "),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
America
Regional Overview
thisbureau <- "Americas"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
# as.character(ctr$CountryAsylumName)
# "Dominican Republic" "Costa Rica"
# "El Salvador" "Guatemala"
# "Honduras" "Mexico"
# "Panama" "Canada"
# "United States of America" "Argentina"
# "Brazil" "Chile"
# "Colombia" "Ecuador"
# "Peru" "Venezuela (Bolivarian Republic of)"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Northern America
Canada
thiscountry <- "Canada"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
USA
thiscountry <- "United States of America"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 1,338,835 persons inUnited States of AmericaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Mexico
thiscountry <- "Mexico"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Central America
Guatemala
thiscountry <- "Guatemala"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 113,975 persons inGuatemalaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
El Salvador
thiscountry <- "El Salvador"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Honduras
thiscountry <- "Honduras"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Costa Rica
thiscountry <- "Costa Rica"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 121,941 persons inCosta RicaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Panama
thiscountry <- "Panama"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 134,391 persons inPanamaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Caribean
Dominican Republic
thiscountry <- "Dominican Republic"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Southern America
Argentina
thiscountry <- "Argentina"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Brazil
thiscountry <- "Brazil"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 425,521 persons inBrazilSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Chile
thiscountry <- "Chile"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Colombia
thiscountry <- "Colombia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Ecuador
thiscountry <- "Ecuador"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Peru
thiscountry <- "Peru"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 1,557,216 persons inPeruSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Venezuela
thiscountry <- "Venezuela (Bolivarian Republic of)"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Asia
Regional Overview
thisbureau <- "Asia"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
#as.character(ctr$CountryAsylumName)
# "Australia" "Uzbekistan" "China"
# "Cambodia" "Malaysia" "Myanmar"
# "Philippines" "Thailand" "Afghanistan"
# "Bangladesh" "India" "Iran (Islamic Republic of)"
# "Pakistan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
East Asia and the Pacific
Australia
thiscountry <- "Australia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 138,260 persons inAustraliaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
China
thiscountry <- "China"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
South Asia
India
thiscountry <- "India"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
South East Asia
Thailand
thiscountry <- "Thailand"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Bangladesh
thiscountry <- "Bangladesh"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Myanmar
thiscountry <- "Myanmar"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Philippines
thiscountry <- "Philippines"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Cambodia
thiscountry <- "Cambodia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 57,456 persons inCambodiaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Malaysia
thiscountry <- "Malaysia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
South West Asia
Iran
thiscountry <- "Iran (Islamic Republic of)"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 800,064 persons inIran (Islamic Republic of)Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Pakistan
thiscountry <- "Pakistan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Afghanistan
thiscountry <- "Afghanistan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Europe
Regional Overview
thisbureau <- "Europe"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
#as.character(ctr$CountryAsylumName)
# [1] "Russian Federation"
# [2] "Ukraine"
# [3] "Estonia"
# [4] "Latvia"
# [5] "Sweden"
# [6] "United Kingdom of Great Britain and Northern Ireland"
# [7] "Bosnia and Herzegovina"
# [8] "Greece"
# [9] "Italy"
# [10] "Serbia"
# [11] "Spain"
# [12] "Azerbaijan"
# [13] "Georgia"
# [14] "Turkey"
# [15] "Austria"
# [16] "Belgium"
# [17] "Germany"
# [18] "Netherlands"
# [19] "Switzerland"
# [20] "France"
# [21] "Norway" Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Western Europe
UK
thiscountry <- "United Kingdom of Great Britain and Northern Ireland"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Germany
thiscountry <- "Germany"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
France
thiscountry <- "France"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Northern Europe
Latvia
thiscountry <- "Latvia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 209,877 persons inLatviaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Estonia
thiscountry <- "Estonia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 73,496 persons inEstoniaSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Norway
thiscountry <- "Norway"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Sweden
thiscountry <- "Sweden"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Southern Europe
Italy
thiscountry <- "Italy"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 184,918 persons inItalySolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Spain
thiscountry <- "Spain"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Greece
thiscountry <- "Greece"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Eastern Europe
Turkey
thiscountry <- "Turkey"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Georgia
thiscountry <- "Georgia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Russian Federation
thiscountry <- "Russian Federation"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
} Gender disaggregation is not available for all of the 81,238 persons inRussian FederationSolution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
MENA
Regional Overview
thisbureau <- "MENA"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
#as.character(ctr$CountryAsylumName)
# [1] "Algeria" "Egypt" "Libya" "Mauritania"
# [5] "Iraq" "Jordan" "Kuwait" "Lebanon"
# [9] "Saudi Arabia" "Syrian Arab Republic" "Yemen" Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Northern Africa
Algeria
thiscountry <- "Algeria"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Egypt
thiscountry <- "Egypt"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Middle East
Jordan
thiscountry <- "Jordan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Syria
thiscountry <- "Syrian Arab Republic"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Western Africa
Regional Overview
thisbureau <- "WestAfrica"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Central Africa
Cameroon
thiscountry <- "Cameroon"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Chad
thiscountry <- "Chad"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Central African Republic
thiscountry <- "Central African Republic"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
West Africa
Burkina Faso
thiscountry <- "Burkina Faso"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Mali
thiscountry <- "Mali"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Niger
thiscountry <- "Niger"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Nigeria
thiscountry <- "Nigeria"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Eastern Africa
Regional Overview
thisbureau <- "EastAfrica"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
#as.character(ctr$CountryAsylumName)Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Great Lakes
Burundi
thiscountry <- "Burundi"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Rwanda
thiscountry <- "Rwanda"East and Horn
Ethiopia
thiscountry <- "Ethiopia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Kenya
thiscountry <- "Kenya"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Somalia
thiscountry <- "Somalia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
##### South Sudan{.tabset}
thiscountry <- "South Sudan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
##### Uganda{.tabset}
thiscountry <- "Uganda"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Tanzania
thiscountry <- "United Republic of Tanzania"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Zambia
thiscountry <- "Zambia"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Sudan
thiscountry <- "Sudan"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Southern Africa
Regional Overview
thisbureau <- "SouthAfrica"
ctr <- selectedctr %>%
filter( UNHCRBureau == thisbureau) %>%
select(CountryAsylumName)
#as.character(ctr$CountryAsylumName)Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(UNHCRBureau == thisbureau &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thisbureau , "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thisbureau ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thisbureau ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(UNHCRBureau == thisbureau & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thisbureau ),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear ) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thisbureau ,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thisbureau ,".html"),
paste0(getwd(),"/decision/decision", thisbureau ,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryOriginCode" = "iso_3")) %>%
filter(UNHCRBureau == thisbureau & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Source Countries hosting Forcibly Displaced People from ",thisbureau ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Southern Africa
South Africa
thiscountry <- "South Africa"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Malawi
thiscountry <- "Malawi"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Mozambique
thiscountry <- "Mozambique"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Angola
thiscountry <- "Angola"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Middle Africa
Democratic Republic of the Congo
thiscountry <- "Democratic Republic of the Congo"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Congo
thiscountry <- "Congo"Categories
#Prepare data
Categories <- end_year_population_totals_long.asy %>%
filter(CountryAsylumName == thiscountry &
Year > (lastyear - 5) ) %>%
group_by(Year, Population.type.label.short ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Categories$Population.type.label.short)) %in% c(2,4), 2,3)
#levels(as.factor(end_year_population_totals_long.asy$Population.type.label.short))
Categories$Population.type.label.short <- factor(Categories$Population.type.label.short,
levels = c("Refugees","Asylum-seekers","Venezuelans Abroad",
"IDPs","Stateless Persons", "Others of Concern"))
#Make plot
ggplot(Categories, aes(x = Year,
y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc"
) + # here we configure that it will be bar chart
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear)) +
facet_wrap( vars(Population.type.label.short ), ncol = ncat) +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "How the different Categories of Forcibly Displaced People are evolving over time?",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Origin
#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
Origin <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryOriginName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Origin, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryOriginName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
subtitle = paste0("Data as of ",lastyear),
x = " ",
y = "# of Forcibly displaced people",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
Demographics
demographics1 <- demographics %>%
dplyr::left_join( unhcrdatapackage::reference %>%
select(UNHCRBureau, iso_3),
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryAsylumName == thiscountry & Year == lastyear-1) %>%
mutate ( totGen = FemaleTotal +MaleTotal,
totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))
tot <- format( sum(demographics1$Total) , big.mark=",")
totprop <- format( round( sum(demographics1$totGen) /
sum(demographics1$Total ) *100,1), big.mark=",")
if (totprop == 0 ) {
cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
} else {
#names(demographics)
pyramid <- demographics1[ demographics1$Year == max(demographics1$Year),
c(
"Female04",
"Female511",
"Female1217",
"Female1859",
"Female60ormore",
"FemaleUnknown",
# "FemaleTotal",
"Male04",
"Male511",
"Male1217",
"Male1859",
"Male60ormore",
"MaleUnknown"#,
# "MaleTotal"
)]
pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
pivot_longer(
cols = Female04:MaleUnknown,
names_to = "Class",
values_to = "Sum",
values_drop_na = TRUE
)
pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
by = list(pyramid2$Class#, pyramid2$REGION_UN
),
sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"
pyramid3 <- pyramid3 %>%
mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
str_detect(Class, "Female") ~ "Female")) %>%
mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
str_detect(Class, "511") ~ "5-11",
str_detect(Class, "1217") ~ "12-17",
str_detect(Class, "1859") ~ "18-59",
str_detect(Class, "60") ~ "60+",
str_detect(Class, "Unknown") ~ "Unknown"))
pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11", "12-17", "18-59", "60+", "Unknown"))
pyramidplot <- ggplot(pyramid3, aes(x = age,
fill = gender,
y = ifelse(test = gender == "Female",
yes = -pc, no = pc)) ) +
geom_bar(stat = "identity") +
geom_label(aes(x = age,
y = ifelse(test = gender == "Female",
yes = -pc -5 , no = pc),
label = paste0(format(round(pc, 1), big.mark=","),"%")
),
hjust = 0,
vjust = 0.5,
colour = "black",
fill = NA,
label.size = NA,
family = "Lato",
size = 5) +
scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
x = "",
y = "Percent of population",
caption = "Source: UNHCR Population Statistics as of end 2020") +
scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
aesthetics = c("colour", "fill")) +
coord_flip()+
unhcRstyle::unhcr_theme(base_size = 14) +
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank())
pyramidplot
}
Solution
#Prepare data
Solution <- solutions_long.asy %>%
filter(CountryAsylumName == thiscountry & Year > (lastyear - 10) ) %>%
group_by(Year, Solution.type.label ) %>%
summarise(Value2 = sum(Value) ) %>%
mutate(Year = as.integer(Year) ) %>%
mutate( valabel = scales::label_number_si()(Value2))
ncat <- ifelse( nlevels(as.factor(Solution$Solution.type.label)) %in% c(2,4), 2,3)
#levels(as.factor(solutions_long.asy$Solution.type.label))
Solution$Solution.type.label <- factor(Solution$Solution.type.label,
levels = c("Naturalisation", "Resettlement arrivals",
"Refugee returns","IDP returns" ))
#Make plot
ggplot(Solution, aes(x = Year, y = Value2 )) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc" ) + # here we configure that it will be bar chart
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
#xlim(c(lastyear-5, lastyear+1)) +
facet_wrap( vars(Solution.type.label ), ncol = ncat) +
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.y = element_line(color = "#cbcbcb"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) + ### changing grid line that should appear
## and the chart labels
labs(title = "Recorded Solutions",
subtitle = paste0(thiscountry),
x = "",
y = "",
caption = "Source: UNHCR.org/refugee-statistics ")
Processing
linksDecision.Ori.Procedure <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
mutate(CountryOriginName = forcats::fct_lump_prop(CountryOriginName, prop = .01, w = Value)) %>%
mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", "")) %>%
## Calculate grouped value for Origin to procedure..
group_by(CountryOriginName, ProcedureName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = CountryOriginName) %>%
rename(target = ProcedureName)
linksDecision.Procedure.Type <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(ProcedureName, DecisionTypeName ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = ProcedureName) %>%
rename(target = DecisionTypeName)
linksDecision.Type.Output <- asylum_decisions_long %>%
## Add reference for the filters
dplyr::left_join( unhcrdatapackage::reference %>%
select(coa_region = `UNHCRBureau`, iso_3), by = c("CountryAsylumCode" = "iso_3")) %>%
filter(coa_region == thisbureau & Year == lastyear & CountryAsylumName == thiscountry) %>%
## Group small records under other
## Calculate grouped value for Origin to procedure..
group_by(DecisionTypeName,Decision.output ) %>%
summarise(value = sum(Value) ) %>%
## Rename variable
rename(source = DecisionTypeName) %>%
rename(target = Decision.output)
linksallDecision <- rbind(linksDecision.Ori.Procedure,
linksDecision.Procedure.Type,
linksDecision.Type.Output )
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodesDecision <- data.frame(
name=c(as.character(linksallDecision$source),
as.character(linksallDecision$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
linksallDecision$IDsource <- match(linksallDecision$source, nodesDecision$name)-1
linksallDecision$IDtarget <- match(linksallDecision$target, nodesDecision$name)-1
# Make the Network
p <- networkD3::sankeyNetwork(Links = linksallDecision,
Nodes = nodesDecision,
Source = "IDsource",
Target = "IDtarget",
Value = "value",
NodeID = "name",
# LinkGroup character string specifying the groups in the Links. Used to color the links in the network.
sinksRight=FALSE) ## If TRUE, the last nodes are moved to the right border of the plot.
# you save it as an html
networkD3::saveNetwork(p, paste0(getwd(),"/decision/decision", thiscountry,".html"))
# you convert it as png -- Need first webshot::install_phantomjs()
webshot::webshot(paste0(getwd(),"/decision/decision", thiscountry,".html"),
paste0(getwd(),"/decision/decision", thiscountry,".png"),
vwidth = 800, vheight = 500)
#getwd()
#Source
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
Source <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long,
y= unhcrdatapackage::reference,
by = c("CountryAsylumCode" = "iso_3")) %>%
filter(CountryOriginName == thiscountry & Year == lastyear) %>%
mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"),
CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>%
group_by( CountryAsylumName, SUBREGION) %>%
summarise(Value2 = sum(Value) ) %>%
mutate( Value3 = scales::label_number_si()(Value2)) %>%
arrange(desc(Value2)) %>%
head(10)
#Make plot
ggplot(Source, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
y = Value2)) +
geom_bar(stat = "identity",
position = "identity",
fill = "#0072bc") + # here we configure that it will be bar chart+
geom_label(aes(x = CountryAsylumName, y = Value2,
#label = format(round(Value2, -3), big.mark=",")),
label = Value3),
hjust = 1,
vjust = 0.5,
colour = "white",
fill = NA,
label.size = NA,
family = "Lato",
size = 6) +
geom_hline(yintercept = 0, size = 1, colour = "#333333") +
coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
## and the chart labels
labs(title = paste0("Top 10 Countries hosting Forcibly Displaced People from ",thiscountry ),
subtitle = paste0("Data as of ",lastyear),
x = "",
y = "",
caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
#scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
scale_y_continuous( label = scales::label_number_si()) + ## Format axis number
geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
unhcRstyle::unhcr_theme(base_size = 14) + ## Insert UNHCR Style
theme(panel.grid.major.x = element_line(color = "#cbcbcb"),
panel.grid.major.y = element_blank()) ### changing grid line that should appear
UnhcrDataPackage - Re-usable/adjustable Source codes for Country Focused Statistical Charts are available Here.
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